On July 20, the Cardinals dropped a game to the Cubs despite going into the ninth inning with a 6–1 lead. Based on Greg Stoll’s win expectancy calculator, when the home team is winning by five runs in the top of the ninth, that’s a victory 99.7% of the time. The Cardinals acted accordingly, bringing in veteran journeyman Luis Garcia for his 2021 debut. This was mop-up duty … until it was not.

Garcia struck out Patrick Wisdom to start the inning, but he was able to reach first base on a dropped third strike. Nico Hoerner followed Wisdom with a single, and Jake Marisnick walked. The odds were still in the Cardinals’ favor; the win expectancy calculator gives the home team in this spot (up five, no outs and the bases loaded) a 97.2% chance of pulling it out. Nevertheless, manager Mike Shildt felt the heat enough to bring in his closer, Alex Reyes. But things did not go as planned. Reyes went walk, strikeout, walk, single, double; a 6–1 lead had turned into a 7–6 deficit in the blink of an eye.

The double did the most damage, but the walks are a theme with Reyes. The surface-level numbers are fantastic; dig one step deeper, and things look a little concerning. On the season, he has posted a 29.3% strikeout rate but also a 19.2% walk rate, leading to a 1.38 WHIP (league average is 1.29) and a 1.53 K/BB ratio that’s about 41% worse than the average pitcher. Reyes’ FIP is 3.68 despite the issues with walks, a testament to his strikeout prowess (led by a slider, curveball, and changeup that generate whiff rates of 46.4%, 57.9%, and 40.0%, respectively, per Baseball Savant) and his ability to induce groundballs with his bowling-ball sinker.

Still though, that walk rate is an issue, but what I want to do here is assuage some of the concerns and help reinforce a point made by Baseball Prospectus’ Jonathan Judge on Twitter just last week: that often a walk or hit-by-pitch is the next best outcome after a strikeout (compared to a ball in play). He noted that while Reyes is toeing the proverbial walk rate line, he has the tools to make that extreme profile work, especially with his ability to generate groundballs with his sinker.

Read the rest of this entry »

Major League Baseball’s sticky stuff crackdown is working. Since the June 3 warning that increased enforcement of the foreign substance rule was coming, spin rates have fallen league-wide. The league-average spin-to-velocity ratio on four-seam fastballs, which sat comfortably above 24.5 rpm/mph for the entirety of the 2020 season and the beginning of the ’21 season, has fallen to under 24 rpm/mph for the first time since the beginning of ’19. This is what that enormous drop looks like visually:

The crackdown has had plenty of consequences, all of which have theoretically had a significant impact on the game. I touched a little bit on one of these outcomes — whether it was fair to ask pitchers to alter their stuff dramatically in the middle of a season — in a July 2 article on Garrett Richards, who claimed that he needed to try “to figure out how to pitch again” post-enforcement. But there has been one outstanding question all along: How will this impact offense? In a year that started with some of the lowest batting averages in baseball history and with run scoring heavily concentrated in home runs, that was of the utmost importance in the minds of baseball-followers, including those who work for the league and for teams. Cubs president Jed Hoyer, for example, called the impact of the sticky substance enforcement “a huge variable” in determining which players Chicago could target at the July 30 trade deadline.

In an article leading up to the changes in enforcement, I covered the potential impact the crackdown would have on offense with a focus on the effect of spin rates on batter performance. The trend was clear: Batters hit much better on four-seam fastballs with less velocity-adjusted spin, and in a world in which fewer pitches are thrown with elite spin, they should have an easier time at the plate. One executive even told Stephanie Apstein and Alex Prewitt of Sports Illustrated that he thought better enforcement of Rule 6.02(c) could actually have an outsized impact on reviving offense around the league, potentially lessening the pressure on baseball to institute rule changes to create more balls in play, higher batting averages, and more non-homer scoring overall. “I think people would be absolutely shocked if they actually enforced this, how much you’ll start to normalize things without rule changes,” they said. Read the rest of this entry »

For the past few days, I’d been searching for a baseball topic to write about. It usually takes less time, but we’re in that calm (if not monotonous) period between the All-Star break and the trade deadline. Ideas are scarcer. Maybe I’d settle on an article with a simple premise?

So I committed myself to tackling pitcher BABIP. (Good going, Justin!)

The notion that pitchers have no control over what happens to a ball in play ushered in a golden age of baseball research, and findings from back then still influence how we view the game today. But over time, we realized that exceptions do exist; for example, Clayton Kershaw consistently allows a below-average BABIP, most likely because he’s a phenomenal pitcher. In addition, certain pitchers have a knack for inducing weak contact in the form of pop-ups or grounders. Exactly how those batted balls impacted BABIP remained a mystery, but you could no longer brush off the metric as total noise.

Years later, Statcast data became available for public use. Even so, research on pitcher BABIP remained far and few between; it’s a daunting subject! I did use two articles as inspiration, however. The first is from FanGraphs user rplunkett97 on our community research page. Dating back to 2017, it mainly discusses a linear model with several variables (BB/9, GB%, Team UZR, and more) used to produce an expected BABIP for each pitcher. The second is courtesy of Alex Chamberlain, also from the same year, who used a mixture of Hard-hit and Barrel rate to create his own version of xBABIP.

Read the rest of this entry »

One of the great batting lines of the first half was Yasmani Grandal’s .189/.388/.436 slash. Unfortunately, as has been the case for many a hitter on the White Sox, his return to action in ‘21 is in doubt after he underwent surgery to repair a knee ligament. I won’t wax poetic on Grandal; Devan Fink did a great job covering his early-season batting line. But it’s becoming more common to see a hitter with an average that starts with a “1” these days. The common reference to a batting average under .200 is the “Mendoza Line,” which our Ashley MacLennon made a strong case for ditching as a reference earlier this season. I, on the other hand, am going to make the case for why it’s become irrelevant.

Batting average, the prevailing measure of a hitter’s success for most of baseball’s existence, has faded into the background, yet the rate at which a hitter successfully reaches base via a hit is still usually the first statistic reported. Grandal’s batting average is not good, but the selection of .200 as a cutoff point is arbitrary; after all, a batting average of .214 is also not good. What most baseball fans understand now is that because all base hits are not equal in value, batting average is limited in what it says about a hitter. But there is a stigma attached to a poor batting average, which is probably why the Mendoza Line has stuck.

Let’s rewind to last year’s shortened campaign. There was a lot of speculation going into a 60-game season as to whether or not a player would be able to hit .400. That didn’t happen, though Charlie Blackmon was hitting .500 after a couple weeks. We did end up with a handful of qualified hitters with an average below .200 — seven such, to be exact:

This is by far the highest number of qualified hitters with a batting average below .200 for a single season. It is totally a product of the short season, though. None of the hitters on the list above are contact hitters, but their true bat-to-ball skills are probably better than what they showed in ‘20. When the sample is small, there is a greater chance that you get some outliers in your results.

Read the rest of this entry »

Last year, Connor Kurcon created dynamic hard-hit rate (DHH%) to add dimension to our typical understanding of Statcast’s hard-hit rate (HardHit%). Whereas HardHit% uses a fixed minimum exit velocity (EV) threshold of 95 mph to determine a hard hit, DHH% uses a — you guessed it — dynamic threshold that changes according to launch angle of the batted ball event (BBE). Kurcon found this orientation of hard-hit rate to be more powerful than its original in terms of describing same-year contact quality (per weighted on-base average on contact, or wOBAcon), predicting next-year contact quality, and predicting itself (year-over-year “stickiness”).

Inspired by a Yermín Mercedes home run off a Willians Astudillo eephus, I borrowed the premise of DHH% and applied it to pitch velocity — that is, the dynamic threshold was based on pitch speed rather than launch angle. Although not as powerful as the original, Pitch DHH% also proved itself superior to HardHit%.

Ever since Kurcon unveiled DHH% in 2020, though, I’ve been thinking about how the premise might apply to spray angle (horizontal angle, lateral angle, whatever you want to call it). It seemed intuitive to me that a hitter would generate more power to his pull side and less to the opposite field. I suspect if you were prompted to guess, you might have said the same. Read the rest of this entry »

There is an interesting quote from Greg Maddux about the relative merits (or, if you’re Maddux, demerits) of “wasting” a pitch in a 0-2 count versus continuing to attack the hitter. Throwing a pitch outside of the zone and hoping for a hopeless swing in an 0-2 count is a baseball convention that’s ingrained in pitchers from the time they are adolescents. The idea is to not give the batter the chance to put the ball in play when the pitcher is in a supremely advantageous position. Maddux eschewed this notion. He said, “The hitter is most vulnerable when you get him in an 0-2 bind. My goal is to take him out immediately. I’m going right after him, not fooling around with wasting a pitch up high or throwing one in the dirt.”

Maddux’s impetus for questioning convention was twofold. First, a waste pitch is (wait for it) a waste. It is a waste of a pitcher’s time and energy and gets him out of rhythm. If you believe that on any given day a pitcher has a finite number of effective pitches in him, then throwing a pitch without the singular purpose of using that pitch to get the batter out is foolhardy. Maddux’s second gripe is that batters have the lowest batting average in 0-2 counts, so why would you fear throwing the ball in or around the strike zone? He also mentions the pitch is usually so far away from the strike zone that the hitter will lay off by default, giving the opposition the opportunity to see one more pitch out of the pitcher’s hand. Maddux is seemingly inferring that seeing this extra pitch assists the batter in timing up a pitcher’s motion, allowing them to gain a small edge in being able to better pick up the ball coming out of the hand.

The merits of a 0-2 waste pitch has been explored in the past. Earlier this yeah, Jim Albert used the same Maddux quote as a jumping off point for evaluating 0-2 pitches at his blog Exploring Baseball Data with R (as an aside, Jim is one of the coauthors of a must-have book if you are interested in getting into baseball analysis). Jim noted that pitchers don’t tend to use fastballs as waste pitches; when pitchers do waste pitches, they are more likely to bury breaking balls below the strike zone. He did note that 0-2 fastballs were located higher than fastballs in other counts, but they still were often in and around the strike zone, and thus were not waste pitches. Back in 2011, John Dewan at Bill James Online found that, in terms of the average plate appearance outcome, there was only a 10th of a run difference in favor of the pitcher between throwing in the strike zone versus outside of it, so there was no clear dominant strategy. Read the rest of this entry »

We’re now five years into the Statcast era, and with that has come a good base of knowledge and an understanding of what small sample events are significant or beyond noise. Alex Chamberlain recently provided a wonderful example of this type of analysis; I encourage you to read that to get a feel for what I’m going to be talking about. But where Alex and Connor Kurcon covered the values of hard-hit balls at extreme launch angles and extreme exit velocity at given pitch speeds, I want to cover foul balls and what we can — or maybe can’t — learn at the extremes.

Any quick look at the Statcast leaderboard will show you that Yermín Mercedes has a max exit velocity of 116.8 mph, good for ninth best in baseball this year. That’s an incredible feat for any player, but what criteria do we want to set when determining a max? We’re ultimately seeking to measure raw power output, so maybe we should be more inclusive to all batted ball events. If we include foul balls, Mercedes would suddenly have the sixth-highest max exit velocity in baseball at 117.7 mph.

I encourage you to listen to that clip with sound, because the play-by-play commentary is all we have as to where the ball landed.

That 0.9-mph jump might not mean much, but there’s more to it once you consider both the rarity of the batted ball and the fact that we have a number on it in the first place. There’s a wide acceptance of all stats derived solely from launch angle and exit velocity, but you should consider the importance of spray angle. In the same way that both Alex and Connor talked about abnormal exit velocities in the context of a pitch speed or launch angle, something similar should be noted when thinking about the spray of the ball.

To understand this relationship, it’s important to see the spray angle at which each player generates their max EV:

Read the rest of this entry »

In 2018, an article by FanGraphs alum Travis Sawchik came with an ominous title: “Go See the Two-Seamer Before It’s Gone.” His instruction alluded to a still-ongoing trend within MLB, whereby numerous pitchers abandon their two-seamers and sinkers in favor of high-spin four-seamers thrown up in the zone. Its impetus boils down to a couple key developments. For one, teams and pitchers wanted to counter batters who adjusted their swing planes to elevate low pitches. They also realized that high fastballs are useful at inducing whiffs, regardless of batters’ tendencies. Furthermore, those fastballs paired well with the breaking ball shapes and locations teams began to covet around the same time.

All in all, the stage was set for a league-wide revolution. You’ve read the stories of how Gerrit Cole and Tyler Glasnow blossomed into superstars using high fastballs. Conversely, you’ve heard the story of how forcing the sinker upon Chris Archer aggravated his struggles. You might have also encountered stories connecting this trend to the recent uptick in strikeouts. The validity of these reports aside, they helped cement a narrative: the four-seamer was in, and the sinker was out.

Three years later, the league doesn’t seem to have veered away from it. Pitchers have located 20% of four-seam fastballs in the upper-third of the zone this season, the highest rate of the Pitch Tracking era (2008 onwards). Meanwhile, two-seamer/sinker usage is the lowest it’s ever been.

Read the rest of this entry »

On May 17, Chicago White Sox legend Yermín Mercedes hit the sixth home run of his stellar, albeit wilting, nevertheless unlikely, rookie campaign. A mammoth blast over the center field wall of Target Field, the home run sparked — in equal parts, seemingly — awe and controversy.

? THE YERMINATOR JUST HOMERED OFF OF LA TORTUGA! ? pic.twitter.com/pHacUxXtdF

— MLB (@MLB) May 18, 2021

The controversy? Mercedes teed off on a 3-0 count with one out to spare in a 15-4 blowout, off a beloved Position Player Pitching^TM no less. He did so in the home park of a sputtering rival, one expected to compete for their division’s title but, at the time, had instead won half as many games (13) as it had lost (26). Naturally, a lengthy and unpleasant discourse about the game’s unwritten rules ensued. Retribution, however juvenile, was had.

At the time, the sheer amount of baggage on the home run did not register with me. My brain is so moldy and soggy that I reacted somewhat primitively. Good lord, Yermín Mercedes absolutely mashed possibly the slowest pitch I’ve ever seen.

Indeed, Mercedes’ home run is the hardest-hit batted ball (109.3 mph) against a pitch 60 mph or slower (47.1 mph) in the Statcast era. Only Christian Walker (seen here) and Ryan McMahon (seen here) come close, and their batted balls came against pitches thrown more than 53 mph. That’s, like, light speed in comparison. Read the rest of this entry »

Swing decisions are generally evaluated with limited nuance. We consider whether the pitch was in the strike zone (as defined by your data provider of choice) and whether the batter swung. Over the course of hundreds or thousands of pitches, this provides an easy-to-comprehend method of effectively evaluating a player’s approach. With a sufficient sample, these binary classifications give us insight into how players approach their plate appearances relative to their peers, which hitters are better at discerning the strike zone and which are more aggressive.

I have a bone to pick, though: there is often no differentiation between pitches that just miss the defined strike zone versus those that miss by multiple feet, or pitches that just nick the strike zone as opposed to pitches right down the middle. A lot of swing decision analysis is done in the binary, but as many analysts have shown, looking at the gradations in the strike zone can be revealing. Granted, this distinction lacks meaning over many pitches; selective hitters with elite batting eyes will separate from their less fastidious peers with respect to chase rate over time. But in smaller samples, the lack of distinction between pitches and their proximity to the strike zone makes judging a player’s swing decisions difficult.

One method we can use is to group pitches by their probability of being called a strike. Similar to how pitches are evaluated for the purpose of studying catcher framing, I created a general additive model for gauging the probability that a given pitch would be called a strike. My model was trivial (relative to the research I linked above) in that I just considered pitch location and pitch movement; for the purpose of this exercise, I thought that would be enough to get the idea across. The model was trained on 80% of pitches called a ball or strike from the 2020 season, with the remaining 20% used as the test set. For the test set, the model was about 92.5% accurate, in that it correctly predicted whether a pitch was called strike 92.5% of the time.

I applied the model to all pitches from the 2019 and ’20 regular seasons, which yielded the probability of a called strike on every pitch. Pitches with higher probabilities of being a called strike if taken are toward the heart of the zone. Pitches at the edges of the zone have anywhere from a 40–60% chance of being called a strike. And pitches with expected probabilities closer to zero are nowhere near the strike zone.

I binned every pitch in increments of 10% of called strike probability. The following represents the swing rates in each of those bins:

As one would imagine, the league as a whole swings at pitches that have higher called strike probabilities; the closer the pitch is to the heart of the zone, the higher that probability. Break those probabilities down even further, and you can see that the chance of a swing increases steadily with called strike probability.

Swing rates increase rapidly as the called strike probability approaches 0 and 100%. For the more competitive pitches, the changes in swing rate are much smaller. Intuitively, you would expect this relationship to be linear throughout the probability interval; for every 1% increase in called strike probability, the swing rate would also increase by some corresponding percent described by the slope of a line regardless of where you are along this interval. This is not the case.

My hunch is that once a pitch reaches a certain threshold of competitiveness (in terms of challenging the hitter to swing), the swing decision is not as tethered to the chance of the pitch being called a strike. Instead, the choice depends on the pitch type and what the hitter is guessing or picks up out of the pitcher’s hand. Addressing the rapid increase in swing rate on the lower end of the spectrum, I would imagine that many of these pitches are thrown in advantageous counts from the perspective of the pitcher — two-strike counts. While the lack of stigma surrounding strikeouts has been talked about ad nauseam in baseball circles, hitters still do not want to strike out. So if these less competitive pitches are often being thrown with two strikes, the swing rate increases are going to be more sensitive to any marginal change in called strike probability. Break it down by count, and you can see that that’s the case:

For the sharp increase on the higher end of the range, my theory is the same as the other end of the spectrum: Pitches approaching a 100% called strike probability are so enticing to swing at that batters will disregard the count to attack them. Murkier pitches will not really be swung at in 2–0, 3–0 or 3–1 counts, but if the pitch is close to an automatic strike, it must be toward the heart of the plate; a batter who has the green light will want to swing.

For context, league-wide swing rates have oscillated between 45–47% over the past decade. Swing rates on pitches with a called strike probability between 40–60% generally fell in this range in 2019 and ’20. It’s the extreme ends of the spectrum where hitter behavior changes most rapidly. We also saw that the count has a significant effect on the swing rates for any given pitch, especially those that were most and least competitive. So, we know the general league-wide trends and we understand why this is a more nuanced method in evaluating swing decisions. What about at the player level? I found a couple of interesting quirks. When you look at the players who are most aggressive on the pitches that are the most advantageous to swing at (those with a called strike probability of at least 90%), you get a mix of players who we think of as having good plate discipline and those who are more free swingers:

Freddie Freeman, Luke Voit, Brandon Lowe, and Corey Seager are all examples of players we generally understand as having good plate discipline. They lay off pitches that have very little chance of resulting in a called strike and attack pitches that can result in positive outcomes on contact. This list also includes Jeff McNeil, Jay Bruce, and Jorge Alfaro, all of whom swing at pitchers at rates higher than league average no matter the location. This type of strategy can work for a player like McNeill, who has displayed throughout his career he is among the league’s best at making contact. For players like Bruce and Alfaro, this is a recipe for either falling out of the league (in the case of Bruce) or finding more time on the bench as time goes on (in the case of Alfaro). On the other end of the spectrum, the analysis is more cut and dry:

Here we have a list of players who we consider either disciplined or passive. These players do a good job of avoiding swinging at bad pitches, but it seems to be more of a product of just not swinging at all. It could also mean that these players are zeroing in on “their pitches to hit,” and can lead to very good seasons (see: Yasmani Grandal, Mitch Garver, and until this season David Fletcher, Brett Gardner, and Eric Sogard) but passing up good pitches can be problematic without either elite power or contact ability (see: Greg Garcia, Harrison Bader before 2021, and Josh VanMeter). This extreme passivity is a fine line to walk; as you can see after great combined 2019-20 seasons, Fletcher, Gardner, and Sogard have fallen off this season after posting very good lines previously. Fletcher especially is one of the best at putting the bat-head on the baseball, but his passivity may be catching up to him as the league has collected more data on his swing patterns.

Finally, here were the most aggressive and passive swingers on pitches with very little chance of becoming a strike:

Unsurprisingly, batters who avoid swinging at the worst pitches tend to post good results. The other end is a bit of a mixed bag. Tim Anderson has gotten away with what we would consider poor swing decisions because of his demonstrated ability to post high-end BABIPs the past few years, a combination of hitting the ball at angles that result in singles and his foot speed. Javier Báez has posted excellent lines (2020 notwithstanding) by slugging his way to success. Without outlier skills, this sort of approach leads to lackluster performance. Before 2020, José Iglesias was not a good hitter in the majors. Kevin Pillar and Hanser Alberto have mostly posted middling results, and I talked about Alfaro’s issues above.

There is not a one-size-fits-all method of approaching plate appearances. A player’s ability to make contact and his power are the driving forces behind how often he should swing and which pitches he should choose to offer at. This conclusion is nothing revelatory but distinguishing swing decisions based on its chance of being a strike if taken gives additional insight into certain players’ plate discipline profiles. Freddie Freeman or Juan Soto, how swing clearly can track the ball very well and we know they have great discipline. But their plate discipline is different than a player like Yasmani Grandal, who has also displayed discipline throughout his career, though that discipline manifests itself in a much more passive approach. When parsing swing decisions by the quality of a pitch on a granular level, players can get by either through aggression or selectivity. This also shows that free-swingers are free-swingers, no matter the pitch. Baseball players and their skills contain multitudes. When we deal with samples in terms of pitches faced, it helps to further parse the information at hand to get a better understanding of how players struggle or perform well.